In Semi-supervised setting, a small number of videos are fully annotated with the temporal boundary of actions and class labels while a large number of videos are either unlabeled or include only video-level labels. Ji et al. [1]} employ a fully supervised framework, known as BSN [2]}, to exploit the small set of labeled data. They encode the input video into a feature sequence and apply sequential perturbations (time warping and time masking [3]}) on it. Then, the student proposal model takes this perturbed sequence as the input but the teacher model predicts directly on the original feature sequence. In the end, the student model is jointly optimized with a supervised loss applied to labeled videos and a consistency loss to all videos.
